Recent pilot studies carried out by our research group attest that supervised and unsupervised Adaptive Models (AMs) such as Artificial Neural Network, Generalized Linear Model, Kohonen Multi-Parametric Self-Organizing Map and Inelastic Collision Model using multimodal MRI image sets can build powerful tools to study acute stroke pathophysiology and to guide stroke therapy. Herein it is proposed to apply all the techniques stated above to existing data sets of human studies gathered from the Stroke Image Repository (STIR) database. This will allow the proposed techniques to be tested and refined, thus selecting a best early predictor of outcome in stroke. STIR subjects will be used as independent training/modeling and validating sets. Once a predictor of untreated outcome is established, an examination of MRI changes will be conducted in both untreated and treated populations. This latter examination will allow the description of the connection of the proposed predictors to clinical measures to be refined. The proposed AMs have shown the ability to approximate the infarcted area in the brain and identify the surrounding tissue at risk. However, no systematic investigation of alternative methods has been made, a deficit we propose to correct. If this effort is successful, we will produce a surrogate MRI outcome measure that will be quickly available (essentially in real time) to robustly predict the outcome MRI image of stroke in the brain at the acute and subacute stages of stroke. This may allow the real-time assessment of treatment effects in acute and subacute stroke patients, thus fundamentally changing treatment paradigms. An important by- product of this study will be the identification of a minimal MRI data set for the prediction of stroke outcome. This will have the effect of standardizing imaging protocols and minimizing the cost of MRI diagnostic studies in acute and subacute stroke.